Traffic in mobile communications networks is expected to exponentially increase during the following years. However, this data boost is not equally distributed, as FIG. 2 illustrates. There are low-density areas 21, where the main target is to improve coverage by Radio Frequency (RF) optimization, while in high-density areas 22 reliable detection of coverage holes is essential to place low-power nodes that improve capacity, which is usually known as Small Cells Design. Not only, but especially in this latter scenario, high accuracy is required.
Operators traditionally have performed these planning and optimization tasks by means of tools that mainly rely on pathloss models, sometimes adjusted by network counters. However, results have been found not accurate enough due to the complexity of capturing the actual propagation patterns, even using ray-tracing. An alternative is the use of drive-tests as disclosed in US 2009/0157342 A1 to sample the actual user experience. Unfortunately, this option, apart from being time-consuming and costly, is very limited in indoor environments, where most of the traffic is, particularly in dense urban areas.
To overcome these limitations, current techniques are based on positioned RF data, either provided by the user with the help of Global Positioning System (GPS) or any other similar technology, i.e. mobile-based positioning, or taken from traces collected by the network, i.e. network-based positioning. However, not all mobiles support GPS and network-based positioning does not require user's consent. Network-based positioning is known and multiple techniques have been proposed, which are mainly based on reported signal strength or time-delay measurements to estimate the mobile location.
A traditional approach is the trilateration based on Received Signal Strength (RSS), where the distance between the mobile and a measured Base Station (BS) is estimated by assuming certain propagation model. The mobile position is given by the intersection of the estimated distance of at least 3 BSs from different sites.
Another possibility is the multilateration based on Observed Time Difference Of Arrival (OTDOA), which estimates the difference in distance between the mobile and two measured BSs by using time-delay measurements. This is mathematically represented by a hyperbola. The mobile position is given by the intersection of at least 2 hyperbolas, so measurements of at least 3 BSs from different sites are required.
Another possibility is the use of Timing Advance (TA) or Propagation Delay (PD), which provides the distance to the service cell, combined with other techniques that estimate the Angle of Arrival (AoA), based, for instance, on comparing RSS differences to the antenna pattern.
Contrary to previous methods, which try to analytically find the mobile position, the fingerprinting consists of building a signal strength map based on collected measurements of the area of study either from GPS or by drive-test campaigns. The mobile position is given by finding the best match to the pre-calculated map. Such fitting can be performed through deterministic or probabilistic approaches.
The term “accuracy” is widely agreed as the Key Performance Indicator (KPI) to evaluate a positioning algorithm, but there is not a unique interpretation for it, so its definition plays here an essential role. At first sight, it seems reasonable to consider it point-to-point, e.g. error distribution (in meters) when tracking a certain user. However, from Small Cells Design point of view, what really matters is not the position of a punctual user, but an accurate overall view of signal strength and traffic in order to identify, for instance, areas with poor coverage or hotspots. It is obvious that very precise point-to-point positioning, i.e. few meters, will lead to very reliable coverage and traffic maps, but is also proved that a small random error, i.e. even lower than 80 m, for instance, due to granularity, leads to totally meaningfulness maps for Small Cells Design.
Unfortunately, such high precision at a reasonable cost becomes very unlike, unless GPS is considered, due the nature of RF measurements. Trilateration based on RSS is prone to fading, multipath, building losses and other propagation distortions. Accurate results would require a very complex propagation model able to capture all these features, which has been proven to be, apart from very time consuming and costly, not realistic for dense urban scenarios.
Regarding OTDOA, time-delay measurements are reported with granularity of 1 chip (i.e. ˜78 m). In an asynchronous network, as UMTS, relative time difference between BSs must be recovered in advance. This is a very challenging task that adds uncertainty. Besides, multipath, especially relevant in dense urban scenarios, can severely distort these time-delay values. Finally, mathematical limitations of the trilateration algorithm, due to geometry and other factors, can make the solution fall into local minima.
Techniques using PD in UMTS are limited by availability because it is only sent at call establishment, and granularity, since it is reported in steps of 3 chips (i.e. ˜274 m). In case of LTE (Long-Term Evolution), frequency and accuracy is higher, but still not enough, since TA is reported with 78 m granularity. Besides, estimating the AoA with enough precision is not trivial, so some extra uncertainty is expected.
Therefore, analytic models that rely on signal and/or time-delay measurements to estimate the user position are unable to provide, even under ideal conditions, enough accuracy for a Small Cells Design in dense urban scenarios. As an alternative, classic fingerprinting can improve accuracy, but requiring extensive surveying campaigns to collect data, which makes it very time consuming and costly.
Accordingly, a need exists to accurately determine a position of a mobile entity in a mobile communications network in order to be able to identify areas with poor coverage or hotspots without using satellite based positioning methods.